.rst
.pdf
Welcome to LangChain
 Contents 
Getting Started
Modules
Use Cases
Reference Docs
LangChain Ecosystem
Additional Resources
Welcome to LangChain#
Large language models (LLMs) are emerging as a transformative technology, enabling
developers to build applications that they previously could not.
But using these LLMs in isolation is often not enough to
create a truly powerful app - the real power comes when you are able to
combine them with other sources of computation or knowledge.
This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
❓ Question Answering over specific documents
Documentation
End-to-end Example: Question Answering over Notion Database
💬 Chatbots
Documentation
End-to-end Example: Chat-LangChain
🤖 Agents
Documentation
End-to-end Example: GPT+WolframAlpha
Getting Started#
Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.
Getting Started Documentation
Modules#
There are several main modules that LangChain provides support for.
For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
These modules are, in increasing order of complexity:
Prompts: This includes prompt management, prompt optimization, and prompt serialization.
LLMs: This includes a generic interface for all LLMs, and common utilities for working with LLMs.
Document Loaders: This includes a standard interface for loading documents, as well as specific integrations to all types of text data sources.
Utils: Language models are often more powerful when interacting with other sources of knowledge or computation. This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.